Method and system for planning optical storage and charging integrated station coupled with electric power traffic

Abstract

The invention provides a planning method and system for an electric power traffic coupled light storage and charging integrated station, and the method comprises the steps: carrying out the prediction of the time-space distribution of a charging load through employing an electric power traffic coupled EV load time-space distribution prediction model, carrying out the distribution of traffic flow according to historical data and an actual road network structure, and simulating the driving and charging characteristic parameters of an EV; carrying out path planning according to the O-D solution of the EV and according to the real-time road resistance matrix and the congestion degree, and judging whether to carry out V2G mode power exchange or not to obtain the spatial and temporal distribution condition of the EV load; and determining an objective function and a day-ahead economic dispatching constraint condition, and solving to obtain planning result data of the optical storage and charging integrated station under the electric power traffic coupling condition. According to the method, the deep coupling characteristic of the power distribution network and the traffic network is considered, and the planning and operation of the optical storage and charging integrated station and the power distribution network are coordinated, so that the optical storage and charging integrated station obtains more considerable efficiency and economic benefits, and the method has better applicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of design of integrated photovoltaic storage and charging stations, and particularly relates to a planning method and system for an integrated photovoltaic storage and charging station coupled with electric power and transportation. Background Art

[0002] Currently, accelerating and prioritizing the development of new infrastructure, with clean transportation energy supply facilities, represented by electric vehicle charging stations, as a key development area, is crucial. Considering that energy storage systems at charging stations can help smooth out peak loads on the power grid, effectively reducing the peak-to-valley difference in grid load, thereby alleviating the pressure to expand the distribution network, EV charging stations with space for photovoltaic modules can quickly consume the electricity generated by these modules. Incorporating a certain amount of photovoltaic energy storage into charging stations can fully leverage the respective advantages of photovoltaics and energy storage, effectively circumventing the limitations of photovoltaic power generation due to its limited sunlight and time, increasing the proportion of clean energy in primary energy, reducing indirect carbon emissions from electric vehicles, and promoting the local consumption of renewable energy.

[0003] With the rapid development of electric vehicles (EVs), the market size is also gradually expanding. Furthermore, as the number of EVs continues to increase, it is essential to rationally plan charging stations to ensure the normal use of EVs. The planning of charging facilities is significantly influenced by the behavior of EVs, so the planning of EV charging stations must fully consider the dynamic characteristics of the transportation network and the distribution of traffic flow. When optimizing the planning of integrated solar-storage-charging stations, it is necessary to consider the deep coupling between the distribution network and the transportation network to achieve significant social and economic benefits.

[0004] Existing research on charging station planning typically considers the integration of energy and geographic information to determine the spatiotemporal distribution of electric vehicle charging loads. The main existing approach is to plan based on node demand. Assuming that electric vehicles charge and discharge at planned nodes, and considering the differences in charging and discharging behavior in different regions, a state transition matrix is further formed through the dynamic behavior of electric vehicles in these regions, load modeling is performed, and the planning and site selection of charging stations are completed. However, in reality, the transfer of electric vehicles between various road nodes is often affected by road congestion. Furthermore, the existing optimization planning process for integrated photovoltaic, storage and charging stations often ignores the impact between the power system and the transportation network, which in turn affects the economic benefits and efficiency of the operation of integrated photovoltaic, storage and charging stations. Summary of the Invention

[0005] The present invention provides a planning method and system for an integrated photovoltaic, storage and charging station coupled with power and transportation. The purpose is to solve the problem that the current planning of integrated photovoltaic, storage and charging stations does not take into account the situation where electric vehicles are transferred between road nodes and are subject to road congestion, and ignores the impact between the power system and the transportation network, which in turn affects the efficiency and economic benefits of the overall operation of the integrated photovoltaic, storage and charging station.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The present invention provides a planning method for an integrated photovoltaic storage and charging station coupled with power and transportation, comprising the following steps:

[0008] S1. Use the electric-traffic coupled EV load spatiotemporal distribution prediction model to predict the spatiotemporal distribution of charging load, thereby obtaining the spatiotemporal distribution of EV load;

[0009] The EV load spatiotemporal distribution prediction model for power-transport coupling is established by building a mathematical model of integrated station equipment based on power-transport coupling and coupling the dynamic transportation network model, distribution network model, and EV charging load model.

[0010] Mathematical model of integrated power and transportation station equipment: By integrating basic data of the power system and transportation system using the EV load spatiotemporal distribution prediction model, a collaborative planning framework for integrated power and transportation stations is formed, and then the model is established within the collaborative planning framework for integrated power and transportation stations;

[0011] The prediction of the spatiotemporal distribution of charging load includes: allocating traffic flow based on historical data and the actual road network structure, simulating EV driving and charging characteristic parameters; performing route planning based on the EV's OD solution and the real-time road resistance matrix and congestion, and determining whether to perform V2G power exchange;

[0012] S2. Determine the objective function and day-ahead economic dispatch constraints based on the spatiotemporal distribution of EV loads, and obtain the planning result data of the integrated photovoltaic storage and charging station under the power-transport coupling conditions through solving them.

[0013] In some implementations, in S2, the dynamic traffic network model specifically includes:

[0014] Road network topology mathematical model:

[0015]

[0016] Where G is the traffic network, V is the set of all network nodes in the traffic network G, E is the set of all road sections in the traffic network G, T represents the set of divided time periods, and W is the set of road section weights, i.e., road resistance.

[0017] User equilibrium status:

[0018]

[0019] Where W ij is the road impedance between ij after reaching equilibrium state; is the link impedance of the link k between the OD pairs ij in the unbalanced state; is the traffic flow on the road segment k between OD pairs ij;

[0020] Establish a user equilibrium model; the road section saturation in each time period is:

[0021]

[0022] Where S a,t is the road saturation of section a, C a is the traffic capacity of section a;

[0023] The corresponding section impedance model and node impedance model are obtained according to different saturations.

[0024] Furthermore, in S1, the section impedance model is:

[0025]

[0026] Where t0 is the zero flow travel time; α and β are impedance influencing factors;

[0027] The node impedance model is:

[0028]

[0029] Where, T tra is the signal cycle of the traffic light at the road intersection; λ is the proportion of the green light duration in the signal cycle; q is the vehicle arrival rate at the intersection node.

[0030] In some embodiments, in S1, based on the distribution network model and according to the functional positioning of the city, the charging and discharging power of the electric vehicles connected to each regional distribution network node k in time period t is accumulated to obtain the corresponding equivalent electric vehicle load:

[0031]

[0032] Where: N represents the number of electric vehicles connected to the node during period t; P k,t is the equivalent electric vehicle load of node k in period t; P k,i,t represents the charging and discharging power of the i-th vehicle at node k during time period t.

[0033] In some embodiments, in S1 , establishing the EV charging load model includes: classifying EVs into commuter private cars, taxis, and public vehicles according to different form characteristics and charging characteristics;

[0034] The OD start-end matrix is used to simulate the starting and ending points of each EV's trip. The shortest path is planned in combination with the real-time road impedance matrix to obtain a set of travel segments. The initial travel time and return time of each EV, as well as the average travel speed, are simulated using Monte Carlo simulation.

[0035] Normal distribution is used to simulate the battery capacity of different types of EVs; different types of EVs correspond to different charging demand conditions;

[0036] EVs are divided into two categories based on whether they accept V2G dispatch:

[0037]

[0038] Where: Ω d and Ω nd They represent the set of electric vehicles that can accept V2G dispatch and those that do not accept V2G dispatch respectively; is the EV charging power; when the return time of the i-th vehicle is t i,d Later than departure time t i,o Travel time The sum of the two means that EV can perform V2G power exchange, otherwise it will always be in one-way charging mode.

[0039] Furthermore, in S1, establishing the EV charging load model also includes: obtaining the average EV queue waiting time and average queue length based on the number of vehicles going to the integrated station for charging per hour:

[0040]

[0041] Where: The number of EVs that need charging; is the average waiting time for EVs; ζ is the number of charging piles in the integrated station; τ EV is the number of EVs that complete the charging process at each charging pile within a unit time, ρ is the service intensity of the charging pile, and L wait For average captain;

[0042] Real-time power consumption of electric vehicles considering ambient temperature and speed The model is:

[0043]

[0044]

[0045] Where: At ambient temperature Tp The vehicle is moving at a speed v EV The power consumption of EV air conditioner after driving S kilometers; T pmax , T pmin are the upper and lower thresholds for turning on the air conditioner respectively; W L and W R are the cooling and heating power of the air conditioner respectively; It is the real-time power consumption per unit mileage at different vehicle speeds.

[0046] In some embodiments, S1 specifically includes: predicting the number of vehicles required for different OD pairs based on historical data, distributing traffic flow in combination with the actual road network structure, thereby obtaining the number of vehicles and congestion degree of each road section, and thus calculating the road resistance matrix within the time period;

[0047] Through Monte Carlo simulation of the driving and charging characteristic parameters of each EV, real-time path planning is performed for the EV based on its OD pair demand and the real-time road resistance matrix using the Dijkstra algorithm. During driving, after each road section, it is determined whether the EV has a charging demand. If so, the integrated station installation node is selected according to the real-time road resistance matrix, the path set is updated, and it is determined whether V2G mode power exchange is performed.

[0048] In some embodiments, S2 specifically includes:

[0049] The objective function includes the annualized comprehensive cost function of the integrated station and the distribution network:

[0050] minC all =C Inv +C om +C FE +Cb uy +C NL +C CL +C BD +C Tra ;

[0051] Where C Inv is the annualized investment and construction cost, C om The operation and maintenance costs of each equipment, C buy Cost of purchasing electricity from the upper grid, C NL is the system network loss cost, C CL The cost of electric energy loss during EV charging and discharging, C BD is the EV battery degradation cost, C Tra Additional transportation costs incurred by dispatching EV loads;

[0052] Second-order cone relaxation is used to process the constraints, and the mixed-integer nonlinear programming model is transformed into a mixed-integer second-order cone programming model for solution, thus obtaining the planning result data of the integrated photovoltaic storage and charging station.

[0053] In some embodiments, in S2, the constraints include system flow constraints, node voltage constraints, branch current constraints, integrated station equipment output constraints, integrated station equipment installation capacity constraints, electric vehicle participation in V2G constraints, electric vehicle charging and discharging state mutual exclusion constraints, integrated station EV charging and discharging equivalent load constraints, EV owner charging satisfaction constraints, electric vehicle load space scheduling constraints, integrated station charging pile installation quantity constraints, and electric vehicle charge state constraints.

[0054] The present invention also provides a planning system for an integrated photovoltaic storage and charging station coupled with power and transportation. The system includes a framework construction module, a prediction model construction module, a charging load model, a load prediction module, and a planning result solution module; wherein:

[0055] The framework construction module is used to build a collaborative planning framework for integrated power and transportation coupling stations based on basic data, and to establish a mathematical model of the integrated power and transportation coupling station equipment within the framework;

[0056] The prediction model building module is used to build a spatiotemporal distribution prediction model of EV load coupled with electricity and transportation;

[0057] The load forecasting module is used to predict the spatiotemporal distribution of charging load and obtain the spatiotemporal distribution of EV load;

[0058] The planning result solving module is used to determine the objective function and the day-ahead economic dispatch constraints, and obtain the planning results of the photovoltaic storage and charging integrated station under the power and transportation coupling conditions through solving.

[0059] Compared with the prior art, the planning method and system of a photovoltaic storage and charging integrated station coupled with power and transportation in the present invention has the following beneficial effects:

[0060] The present invention provides a planning method for an integrated photovoltaic storage and charging station coupled with power and transportation, comprising the following steps: S1, using a power and transportation coupled EV load spatiotemporal distribution prediction model to predict the spatiotemporal distribution of the charging load, thereby obtaining the spatiotemporal distribution of the EV load; wherein, the power and transportation coupled EV load spatiotemporal distribution prediction model is established by establishing a mathematical model of the integrated station equipment based on power and transportation coupling and coupling a dynamic transportation network model, a distribution network model, and an EV charging load model; the mathematical model of the integrated station equipment coupled with power and transportation is formed by integrating the basic data of the power system and the transportation system by using the EV load spatiotemporal distribution prediction model. , forming a collaborative planning framework for an integrated station coupled with electricity and transportation, and then establishing the obtained one within the collaborative planning framework for an integrated station coupled with electricity and transportation; the prediction of the spatiotemporal distribution of charging load includes: allocating traffic flow according to historical data and in combination with the actual road network structure, simulating the driving characteristic parameters and charging characteristic parameters of EV; solving the OD of EV and planning the path according to the real-time road resistance matrix and congestion, and judging whether to perform V2G mode power exchange; S2, determining the objective function and the day-ahead economic dispatch constraints based on the spatiotemporal distribution of EV load, and obtaining the planning result data of the photovoltaic storage and charging integrated station under the conditions of electricity and transportation coupling by solving. Based on the above, the present invention establishes a collaborative planning model for an integrated station coupled with electricity and transportation, which takes into account the deep coupling characteristics of the distribution network and the transportation network, and accumulates the charging and discharging power of electric vehicles connected to the distribution network to obtain the corresponding equivalent electric vehicle load. The model coordinates the planning and operation of the photovoltaic storage and charging integrated station and the distribution network, thereby enabling the photovoltaic storage and charging integrated station to achieve better efficiency and economic benefits.

[0061] While coupling energy with geographic information, this invention fully considers traffic congestion constraints and provides a more detailed description of the dynamic characteristics of the transportation network. Based on real-time traffic flow, this invention derives a dynamic equilibrium solution for vehicle flow and, through the network segment impedance and node impedance models, a real-time road resistance matrix. This is then combined with a dynamic path planning algorithm to update the driving path of electric vehicles. Based on charging demand, the method determines whether to update the path set and whether to perform V2G (vehicle-to-grid) power exchange. This improves the cost-effectiveness and accuracy of planning and provides technical support for the overall operation and maintenance of integrated photovoltaic, storage, and charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0063] Figure 1 A schematic diagram of a planning method for a power-transport coupled photovoltaic storage and charging integrated station and a power-transport coupled collaborative optimization framework in the system according to the present invention;

[0064] Figure 2 This is a flow chart of generating the load distribution of electric vehicles in a typical day in a V2G environment in a planning method and system for a photovoltaic storage and charging integrated station coupled with electricity and transportation according to the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0066] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0067] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0068] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0070] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0071] How to coordinate the planning and operation of integrated photovoltaic, storage and charging stations with the distribution network to reduce the planning costs of integrated photovoltaic, storage and charging stations and improve their operating efficiency.

[0072] like Figure 1 and Figure 2 As shown, the present invention provides a planning method for an integrated photovoltaic storage and charging station coupled with power and transportation, comprising the following steps:

[0073] S1. Use the electric-traffic coupled EV load spatiotemporal distribution prediction model to predict the spatiotemporal distribution of charging load, thereby obtaining the spatiotemporal distribution of EV load;

[0074] The EV load spatiotemporal distribution prediction model for power-transport coupling is established by building a mathematical model of integrated station equipment based on power-transport coupling and coupling the dynamic transportation network model, distribution network model, and EV charging load model.

[0075] Mathematical model of integrated power and transportation station equipment: By integrating basic data of the power system and transportation system using the EV load spatiotemporal distribution prediction model, a collaborative planning framework for integrated power and transportation stations is formed, and then the model is established within the collaborative planning framework for integrated power and transportation stations;

[0076] The prediction of the spatiotemporal distribution of charging load includes: allocating traffic flow based on historical data and the actual road network structure, simulating EV driving and charging characteristic parameters; performing route planning based on the EV's OD solution and the real-time road resistance matrix and congestion, and determining whether to perform V2G power exchange;

[0077] S2. Determine the objective function and day-ahead economic dispatch constraints based on the spatiotemporal distribution of EV loads, and obtain the planning result data of the integrated photovoltaic storage and charging station under the power-transport coupling conditions through solving them.

[0078] This invention considers the deep coupling between the distribution network and the transportation network when optimizing the planning of integrated photovoltaic, storage, and charging stations, in order to achieve substantial social and economic benefits. This invention distributes traffic based on real-time vehicle travel demand, guides vehicle route planning, selects optimal charging locations for electric vehicles with charging needs, and superimposes the electric vehicle load at the integrated station installation node with the base load. This establishes a collaborative optimization planning model for the integrated station and the distribution network, enabling site selection and sizing of the integrated station, thereby improving overall economic benefits and efficiency.

[0079] The following further describes in detail a planning method and system for an integrated photovoltaic storage and charging station coupled with power and transportation according to the present invention through specific embodiments.

[0080] like Figure 1 As shown, the collaborative optimization framework for the electric-transportation coupling system of the present invention primarily involves the following: the distribution network layer transmits information such as charging electricity prices and charging scheduling compensation prices to the transportation network layer. The transportation network layer then distributes traffic based on real-time vehicle travel demand, guides vehicle routing based on real-time road congestion information, and selects optimal charging locations for electric vehicles in need, effectively alleviating traffic congestion. The transportation network transmits the electric vehicle load power at each integrated station installation node to the coupled distribution network node, where it is added to the node's base load to obtain the total node load.

[0081] The present invention provides a planning system for an integrated photovoltaic storage and charging station coupled with electric power and transportation. The system includes a framework construction module, a prediction model construction module, a charging load model, a load prediction module, and a planning result solution module.

[0082] The framework construction module is used to build a collaborative planning framework for integrated power and transportation coupling stations based on basic data, and to establish a mathematical model of the integrated power and transportation coupling station equipment within the framework;

[0083] The prediction model building module is used to build a spatiotemporal distribution prediction model of EV load coupled with electricity and transportation;

[0084] The load forecasting module is used to predict the spatiotemporal distribution of charging load and obtain the spatiotemporal distribution of EV load;

[0085] The planning result solution module determines the objective function and day-ahead economic dispatch constraints, and solves them to obtain the planning results for integrated photovoltaic, storage, and charging stations under power-transport coupled conditions. This system provides a platform for planning integrated photovoltaic, storage, and charging stations under power-transport coupled conditions.

[0086] During the implementation of the planning method for the integrated photovoltaic storage and charging station coupled with electric power and transportation of the present invention, the process can be as follows:

[0087] Establish a collaborative planning framework for integrated stations coupled with power and transportation; establish a mathematical model for the equipment in the integrated station coupled with power and transportation; establish a spatiotemporal distribution prediction model for EV load coupled with power and transportation; determine the objective function and day-ahead economic dispatch constraints, and solve the planning of integrated photovoltaic, storage and charging stations that realize power and transportation coupling.

[0088] In some embodiments, the main equipment in the integrated power and transportation coupling station includes photovoltaic cells, energy storage equipment, electric vehicle batteries, and a photovoltaic uncertainty factor model.

[0089] In some embodiments, the mathematical model of each device operation is established as follows:

[0090] 1. Photovoltaic cell model;

[0091] The output power of a photovoltaic device can be expressed as follows:

[0092]

[0093] Where: P PV is the output power of the photovoltaic device; P STC is the power of photovoltaic equipment under standard temperature and light conditions; is the actual light intensity; is the standard light intensity; k is the temperature coefficient; T PV and T ref are actual temperature and standard temperature respectively; A r is the area of the photovoltaic panel; η PV is the efficiency of the photovoltaic panel.

[0094] 2. Energy storage equipment model;

[0095] The electric energy storage device model can be expressed as:

[0096]

[0097] Where: E B,t 、E B,t-1 are the capacities of the energy storage device at time t and t-1 respectively; τ is the energy storage loss coefficient; P Bch,t 、P Bdis,t are the charging and discharging power of the energy storage device at time t; η ch ,η dis are the charging and discharging energy efficiencies, respectively.

[0098] 3. Electric vehicle battery model;

[0099] Taking into account the battery degradation effect, factors such as the frequency and charge-discharge rate of charging and discharging will affect the service life of the battery.

[0100] The best fitting curve formula for the battery life curve is as follows:

[0101] L B (DOD) = a × DOD -b ×e -c·DOD ;

[0102] Taking a discharge event as an example, within a time interval Δt, the average output power of the battery is P Bev,t , then the depth of discharge DOD in this time interval can be expressed as follows:

[0103]

[0104] Where: Represents the actual full capacity of the electric vehicle battery at time t.

[0105] After a charge and discharge event, the actual full capacity of the battery will decrease. The actual full capacity of the battery at time t+Δt can be combined with the factory rated capacity of the battery. Calculate according to the following formula:

[0106]

[0107] According to the actual full capacity The SOC value of the electric vehicle battery can be obtained

[0108]

[0109] Where: are the capacity of the electric vehicle battery at time t-1; τ EV is the energy storage loss coefficient; They are respectively the charging and discharging power of the electric vehicle battery at time t; are the charging and discharging energy efficiencies of electric vehicle batteries respectively.

[0110] 4. Modeling of uncertainty factors in Optical Valley;

[0111] Due to the impact of photovoltaic uncertainty on the system, the Beta distribution is used to describe the probability density distribution of photovoltaic output:

[0112]

[0113]

[0114] Where: P PV is the actual photovoltaic power; P PV，max is the maximum photovoltaic power; Γ(·) is the gamma function; α and β are the shape parameters of the beta function; μ is the mathematical expectation of the beta distribution; σ2 is the variance of the Beta distribution.

[0115] In some embodiments, the process of establishing the above electric-traffic coupled EV load spatiotemporal distribution prediction model is as follows:

[0116] 1. Dynamic transportation network model;

[0117] The mathematical model of road network topology is described as:

[0118]

[0119] Where: G is the traffic network; V is the set of all network nodes in the traffic network G; E is the set of all road sections in the traffic network G; T represents the set of divided time periods, that is, the whole day is divided into m time periods; W is the set of road section weights, that is, road resistance.

[0120] The road resistance impedance of urban roads is:

[0121] Wi j,t =ZP i,t +ZLi j,t ;

[0122] Where ZP i,t represents the impedance model of node i at time t; ZL ij,t Represents the impedance model of section ij at time t.

[0123] The user equilibrium state is:

[0124]

[0125] Where: W ij is the road impedance between ij after reaching equilibrium state; is the link impedance of the link k between the OD pairs ij in the unbalanced state; is the traffic flow on the road segment k between OD pairs ij.

[0126] Based on the following assumptions: 1) the impedance of a road section is a function of the traffic flow of that road section and is not affected by the traffic flow of other roads; 2) the impedance of a road section is assumed to be a strictly increasing function of the traffic flow. The user equilibrium model is established as:

[0127]

[0128] Among them, q ij is the traffic flow from i to j, f a is the traffic flow on section a, c a is the impedance on section a, is a 0-1 variable. If section a is on the k-th path connecting the O-D pair i-j, its value is 1; otherwise, it is 0.

[0129] The saturation of each section in each time period is:

[0130]

[0131] In the formula: S a,t is the saturation of section a, and C a is the traffic capacity of section a.

[0132] According to the urban traffic condition division standard, the saturation S evaluation index: unobstructed (0 < S ≤ 0.6), slow-moving (0.6 < S ≤ 0.8), crowded (0.8 < S ≤ 1.0), and severely congested (1.0 < S ≤ 2.0). According to different saturations, the corresponding section impedance and node impedance models are obtained.

[0133] 1) The section impedance model is;

[0134]

[0135] In the formula: t0 is the zero-flow travel time; α, β are impedance influence factors.

[0136] 2) The node impedance model is;

[0137]

[0138] In the formula: T tra is the signal cycle of the traffic signal at the road intersection; λ is the proportion of the green light duration in the signal cycle; q is the vehicle arrival rate at the intersection node.

[0139] 2. Distribution network model:

[0140] Based on the IEEE33 standard distribution network model, and the area is divided according to the functional positioning of the city. The charging and discharging power of the electric vehicles connected to each distribution network node k at time t is accumulated to obtain the corresponding equivalent electric vehicle load:

[0141]

[0142] In the formula: N represents the number of electric vehicles connected to the node at time t; P k,t is the equivalent electric vehicle load of node k at time t; P k,i,t represents the charging and discharging power of the i-th vehicle at node k within time t.

[0143] 3. EV charging load model:

[0144] In the embodiment, the present invention divides electric vehicles into three categories according to different form characteristics and charging characteristics: commuting private cars, taxis and public vehicles.

[0145] 1) Commuter Private Cars: These EVs primarily operate in residential and office areas, with minimal route variation and relatively fixed commuting times. Furthermore, these EVs spend extended periods at integrated stations, with a significant proportion charging in V2G mode.

[0146] 2) Taxis: The travel time and OD pairs of this type of EV are highly random, and the driving routes vary greatly. The charging locations are not fixed and the stay time is short. They are rarely charged in V2G mode.

[0147] 3) Other public vehicles: These vehicles mainly include commercial vehicles, official private vehicles, and sanitation vehicles. Their travel times and OD pairs are highly random, and their charging times and locations are not fixed.

[0148] The driving characteristics of EV use the OD matrix method, and its start and end probability matrix is:

[0149]

[0150] Where: P ij is the travel probability with ij as the OD pair.

[0151] The OD start-end matrix is used to simulate the starting and ending points of each EV's trip. The Dijkstra algorithm is used in combination with the real-time road impedance matrix to perform shortest path planning and obtain the set of travel sections:

[0152]

[0153] Where: v ij =1 means that the road segment ij is in the actual driving path d i (i) in the set, otherwise v ij = 0. Finally, the initial travel time t of each EV is simulated by Monte Carlo. 0,i and return time t D,i and average driving speed

[0154] The present invention uses normal distribution to simulate the battery capacity of different types of electric vehicles:

[0155]

[0156] Where: C EV,i is the battery capacity of the i-th EV; u and σ are the mean and standard deviation of the normal distribution function. In addition, the initial state of charge (SOC) of the EV also follows a normal distribution.

[0157] The remaining power of the i-th EV at time t is:

[0158] C i,t =η(C i,t-1 -Δl·γ EV );

[0159] Where: η is the energy consumption coefficient, which is 0.9-1; γ EV is the power consumption coefficient of electric vehicles, usually 0.15-0.2 (kW·h / km); C i,t , C i,t-1 is the remaining power of the i-th vehicle at time t and time t-1; Δl is the EV driving distance during this period.

[0160] Different types of EVs correspond to different charging demand conditions, as shown below.

[0161] 1) Commuter car: When the vehicle arrives at the destination, the current remaining power C i,t When the return trip demand cannot be met, the charging demand is triggered, that is:

[0162] C i,t ≤L i,t,O γ EV ;

[0163] Where, L i,t,O Indicates the current location of the i-th EV and the origin O i The distance between them.

[0164] 2) Taxi: If the remaining power is C during driving i,t When the charge threshold is lower than the charging threshold, a charging request is triggered.

[0165] C i,t ≤ε·C EV,i ;

[0166] Where ε is the charging threshold coefficient, which ranges from 0.25 to 0.35.

[0167] 3) Other public vehicles: When performing routine tasks, they can refer to the first type of EV and mainly charge at the destination and origin. When performing temporary tasks, they can refer to the second type of EV. If the need for charging is triggered during driving, they will select an integrated station for charging based on the real-time road resistance matrix.

[0168] The electric vehicles involved in this invention are divided into two categories according to whether they accept V2G scheduling:

[0169]

[0170] Where: Ω d and Ωnd They represent the set of electric vehicles that can accept V2G dispatch and those that do not accept V2G dispatch respectively; is the EV charging power. When the return time of the i-th vehicle is t i,d Later than departure time t i,o Travel time The sum of the two means that EV can perform V2G power exchange, otherwise it will always be in one-way charging mode.

[0171] Based on the number of vehicles going to the integrated charging station per hour, the average waiting time and average queue length of EVs are as follows:

[0172]

[0173] Where: The number of EVs that need charging; is the average waiting time in queue for EVs; is the number of charging piles in the integrated station; τ EV is the number of EVs that complete the charging process at each charging pile within a unit time, i.e., the inverse of the EV charging time; ρ is the charging pile service intensity, which is less than 1; L wait For the average captain.

[0174] Real-time power consumption of electric vehicles considering ambient temperature and speed The model is:

[0175]

[0176]

[0177]

[0178] Where: At ambient temperature T p The vehicle is moving at a speed v EV The power consumption of EV air conditioner after driving S kilometers; T pmax , T pmin are the upper and lower thresholds for turning on the air conditioner respectively; W L and W R are the cooling and heating power of the air conditioner respectively; It is the real-time power consumption per unit mileage at different vehicle speeds.

[0179] 4. The method for predicting the spatiotemporal distribution of charging load is as follows:

[0180] like Figure 2As shown, the present invention first obtains the driving and charging characteristics of each EV through the above steps. The number of vehicles required for different OD pairs is predicted based on historical data, and traffic flow is distributed based on the actual road network structure to obtain the number of vehicles and congestion level for each road section. The road resistance matrix for that period is then calculated based on the dynamic transportation network model. With a time step of 1 hour, there are 24 road resistance matrices in a single day. Monte Carlo simulation is performed on each EV's driving and charging characteristics. Based on its OD pair demand and the real-time road resistance matrix, a real-time route is planned for the EV using the Dijkstra algorithm. During driving, after each road section, it is determined whether the EV requires charging. If so, an integrated station installation node is selected based on the real-time road resistance matrix and the route set is updated. Otherwise, the EV continues to the next road section. When the EV arrives at its destination, if the destination is an integrated station installation node, it is determined whether the EV should perform V2G power exchange.

[0181] As a specific embodiment, the present invention constructs an integrated station collaborative planning and optimized operation model for power and transportation coupling.

[0182] 1. Construct an objective function to minimize the annualized combined cost of the integrated station and distribution network;

[0183] minC all =C Inv +C om +C FE +C buy +C NL +C CL +C BD +C Tra ;

[0184] Where C Inv is the annualized investment and construction cost, C om The operation and maintenance costs of each equipment, C buy Cost of purchasing electricity from the upper grid, C NL is the system network loss cost, C CL The cost of electric energy loss during EV charging and discharging, C BD is the EV battery degradation cost, C Tra Additional transportation costs incurred by dispatching EV loads.

[0185] 1) Annualized investment and construction cost:

[0186]

[0187]

[0188] Where: Represents the unit capacity / quantity investment and construction costs of photovoltaic equipment, energy storage equipment, and electric vehicle charging piles respectively; N bus Indicates the total number of nodes in the distribution network; Respectively represent the capacity / quantity of photovoltaic equipment, energy storage equipment, and charging piles installed at distribution network node i; χ PV , χ ST , χ CS is the auxiliary variable for annualized calculation of investment and construction costs of each equipment; d is the discount rate, l m is the economic life of the mth distributed power source.

[0189] 2) Annual operation and maintenance costs:

[0190]

[0191] Where: represent the annual operation and maintenance cost coefficients of photovoltaic equipment and energy storage equipment respectively; The annual operation and maintenance cost coefficient of the unit number of charging piles; They represent the operating power of the photovoltaic equipment and energy storage equipment at the distribution network node i during period t on a typical working day in season s; They represent the operating power of the photovoltaic equipment and energy storage equipment at the distribution network node i during period t on a typical weekend day in season s; Δt is the simulation time step, which is set to 1h in the present invention.

[0192] 3) Annual electricity purchase cost from the higher-level power grid:

[0193]

[0194] Where: Ω con is the set of nodes connected to the upper power grid; Ω down,i is the set of downstream connected nodes of distribution network node i; σ buy,t Real-time electricity price for purchasing electricity from the higher-level power grid; They represent the active power transmitted by distribution network branch ij during period t on typical weekdays and weekends in season s.

[0195] 4) Annual system network loss cost:

[0196]

[0197] Where: σ NL is the network loss cost coefficient; They represent the square value of the current flowing through the distribution network branch ij during the typical midday period t on weekdays and weekends in season s; R ij is the equivalent resistance of the transmission line of the distribution network branch ij.

[0198] 5) Annual EV charging and discharging energy loss cost:

[0199]

[0200] Where: σ CL is the EV charging and discharging energy loss cost coefficient; η is the EV charging and discharging energy loss rate; are the EV equivalent load powers at distribution network node i during period t on typical weekdays and weekends, respectively.

[0201] 6) Annual EV battery degradation cost:

[0202]

[0203] Where: σ BD is the battery degradation loss coefficient during the EV charging and discharging process.

[0204] 7) Additional transportation costs incurred each year due to dispatching electric vehicle loads:

[0205]

[0206] Where: C Tra Additional transportation costs incurred for dispatching and guiding EVs to the integrated station; denote the number of EVs arriving at road network node i on typical weekdays and weekend days in season s; Ω CS The candidate node set configured for the integrated station; d ij is the distance between nodes ij in the road network; They represent the number of EVs located at road network node i heading to integrated station node j on typical weekdays and weekend days in season s, respectively, and take values of 0 or 1.

[0207] 2. The constraints of the model include:

[0208] 1) System power flow constraints:

[0209]

[0210]

[0211]

[0212]

[0213] Where: Ω up,j ,Ω down,j The set of upstream / downstream nodes connected to node j respectively; P s,t,ij , Qs,t,ij They represent the active and reactive power transmitted by the distribution network branch ij in the season s period t respectively; They represent the equivalent active and reactive loads of the distribution network node j in the period t during the season s, respectively; Ω P is the node set of the distribution network; Pl oad,s,t,j , Q load,s,t,j They represent the active and reactive loads of the distribution network node j in the period t during the season s; P PV,s,t,j , Q PV,s,t,j They represent the active and reactive power of the photovoltaic equipment at the distribution network node j in the season s period t; P ST,s,t,j is the active power output of the energy storage device at the distribution network node j in the period t of season s; P EV,s,t,j is the EV charging and discharging equivalent load at distribution network node j in season s and period t.

[0214] 2) Node voltage constraints;

[0215]

[0216]

[0217] Where: U s,t,i is the voltage amplitude of the distribution network node i in the period t of season s; X ij is the equivalent reactance of the transmission line of the distribution network branch ij; Ω L is the distribution network branch set; U sub is the voltage amplitude of the node connected to the upper power grid, which is usually 1.0pu; U min 、U max They are the lower and upper limits of the allowable voltage fluctuation range respectively.

[0218] 3) Branch current constraints:

[0219]

[0220]

[0221] Where: It is the square of the maximum current allowed to flow through branch ij.

[0222] 4) Constraints on the installation capacity of integrated station equipment:

[0223]

[0224] Where: They represent the number of photovoltaic devices and energy storage devices installed at distribution network node i respectively; Respectively represent the rated capacity of the unit quantity of photovoltaic equipment and energy storage equipment; Ω PV,Ω ST A set of candidate installation nodes for integrated stations in the distribution network.

[0225] 5) Output constraints of integrated station equipment:

[0226]

[0227] Where: It is the output constraint of photovoltaic equipment per unit quantity, and its value is related to the solar radiation intensity and temperature.

[0228] 6) Restrictions on electric vehicles participating in V2G:

[0229]

[0230] Where: are the charge and discharge status flags of the kth EV located at the distribution network node i in the season s period t, with the values being 0 or 1; They represent the arrival time and expected stay time of the kth EV at distribution network node i, respectively.

[0231] 7) Mutually exclusive constraints on electric vehicle charging and discharging states:

[0232]

[0233] 8) Integrated station EV charging and discharging equivalent load constraints:

[0234]

[0235] Where: The set of origin nodes for EVs heading to the integrated station to install node j for charging; is the set of electric vehicle serial numbers at the node with charging demand in season s period t; Ω CS A collection of candidate nodes for the integrated station installation.

[0236] 9) EV owners’ charging satisfaction constraints:

[0237]

[0238] Where: For the period t in season s, The expected power of the kth electric vehicle arriving at node i at the end of charging; For the period t in season s, The remaining power of the kth electric car arriving at node i at time; Ceil() is a function rounded toward positive infinity; Rated charging power for electric vehicle batteries.

[0239] 10) Electric vehicle state of charge constraints:

[0240]

[0241] Where: For the period t in season s, The rated capacity of the battery of the kth electric vehicle arriving at node i at time; Floor() is a function that rounds toward negative infinity.

[0242] 11) Constraints on the number of charging piles installed at integrated stations:

[0243]

[0244] Where: N CS,s,t,j is the number of charging piles required at node j in season s and period t; ω CS,min The minimum number of charging piles to be installed meets the coefficient.

[0245] 12) Electric vehicle load space scheduling constraints:

[0246]

[0247]

[0248] Where, ξ s,i,k,j W is the spatial scheduling state flag of the kth EV from node i to node j, which takes a value of 0 or 1; ij and W lim are the real-time road resistance of section ij and the upper limit of the road resistance that can be accepted for scheduling.

[0249] In order to improve the solution efficiency while ensuring accuracy, the present invention performs second-order cone relaxation on some of the above constraints, specifically including:

[0250] Perform variable substitution based on the following formula:

[0251]

[0252]

[0253] This transforms the constraints into:

[0254]

[0255] Relax the equal sign in the formula to a greater than or equal sign, and its 2-norm form is:

[0256]

[0257] The present invention adopts CPLEX, Gurobi and other commercial solvers and heuristic algorithms to jointly solve the objective function.

[0258] In summary, the present invention provides a planning method and system for an integrated photovoltaic, storage and charging station that couples electricity and transportation. The integrated photovoltaic, storage and charging station mainly includes photovoltaic cells, energy storage equipment, electric vehicle batteries, and uncertainties brought by photovoltaics. The EV load spatiotemporal distribution prediction method couples the distribution network with the transportation network, takes into account the road network information at the sections and nodes of the transportation network, performs dynamic flow distribution and obtains the road resistance matrix, and combines the real-time Dijkstra dynamic path planning algorithm to update the driving path of the electric vehicle and simulate its driving process and charging behavior. The optimization model takes the lowest annualized comprehensive cost as the goal, comprehensively considers the distribution network, transportation network and the coupling relationship between them, establishes corresponding constraints and designs corresponding solution algorithms. The present invention establishes a collaborative planning model for an integrated photovoltaic station coupled with electricity and transportation, accumulates the charging and discharging power of electric vehicles connected to the distribution network to obtain the corresponding equivalent electric vehicle load. In addition, based on the real-time traffic flow, a dynamic equilibrium solution for traffic flow is obtained, and the real-time road resistance matrix is obtained through the traffic network segment impedance and node impedance model. The driving path of the electric vehicle is updated in combination with the Dijkstra dynamic path planning algorithm. According to the charging demand, it is decided whether to update the path set and whether to perform V2G mode power exchange, thereby coordinating the planning and operation of the photovoltaic storage and charging integrated station and the distribution network, reducing the planning cost of the photovoltaic storage and charging integrated station system, and improving the system's operating efficiency, with better practicality.

[0259] Finally, it should be noted that the above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in this industry can smoothly implement the present invention as shown in the specification and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with this profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the scope of protection of the technical solution of the present invention.

Claims

1. A planning method for an integrated photovoltaic storage and charging station coupled with power and transportation, characterized in that: The steps include: S1. Use the electric-traffic coupled EV load spatiotemporal distribution prediction model to predict the spatiotemporal distribution of charging load, thereby obtaining the spatiotemporal distribution of EV load; The EV load spatiotemporal distribution prediction model for power-transport coupling is established by building a mathematical model of integrated station equipment based on power-transport coupling and coupling the dynamic transportation network model, distribution network model, and EV charging load model. Mathematical model of integrated power and transportation station equipment: By integrating basic data of the power system and transportation system using the EV load spatiotemporal distribution prediction model, a collaborative planning framework for integrated power and transportation stations is formed, and then the model is established within the collaborative planning framework for integrated power and transportation stations; The prediction of the spatiotemporal distribution of charging load includes: allocating traffic flow based on historical data and the actual road network structure, simulating EV driving and charging characteristic parameters; performing route planning based on the EV's OD solution and the real-time road resistance matrix and congestion, and determining whether to perform V2G power exchange; S2. Determine the objective function and day-ahead economic dispatch constraints based on the spatiotemporal distribution of EV loads, and obtain the planning result data of the integrated photovoltaic storage and charging station under the power-transport coupling conditions through solving them.

2. The planning method for an integrated photovoltaic storage and charging station coupled with power and transportation according to claim 1 is characterized in that: In S1, the dynamic traffic network model specifically includes: Road network topology mathematical model: Where G is the traffic network, V is the set of all network nodes in the traffic network G, E is the set of all road sections in the traffic network G, T represents the set of divided time periods, and W is the set of road section weights, i.e., road resistance. User equilibrium status: Where W ij is the road impedance between ij after reaching equilibrium state; is the link impedance of the link k between the OD pairs ij in the unbalanced state; is the traffic flow on the road segment k between OD pairs ij; Establish a user equilibrium model; the road section saturation in each time period is: Where S a,t is the road saturation of section a, C a is the traffic capacity of section a; The corresponding section impedance model and node impedance model are obtained according to different saturations.

3. The planning method for an integrated photovoltaic storage and charging station coupled with power and transportation according to claim 2 is characterized in that: In S1, the road section impedance model is: Where t0 is the zero flow travel time; α and β are impedance influencing factors; The node impedance model is: Where, T tra is the signal cycle of the traffic light at the road intersection; λ is the proportion of the green light duration in the signal cycle; q is the vehicle arrival rate at the intersection node.

4. The planning method for an integrated photovoltaic storage and charging station coupled with electric power and transportation according to claim 1 is characterized in that: In S1, based on the distribution network model and according to the functional positioning of the city, the region is divided, and the charging and discharging power of the electric vehicles connected to each regional distribution network node k in time period t is accumulated to obtain the corresponding equivalent electric vehicle load: Where: N represents the number of electric vehicles connected to the node during period t; P k,t is the equivalent electric vehicle load of node k in period t; P k,i,t represents the charging and discharging power of the i-th vehicle at node k during time period t.

5. The planning method of the integrated photovoltaic storage and charging station coupled with power and transportation according to claim 1 is characterized in that: In said S1, establishing the EV charging load model includes: classifying EVs into commuter private cars, taxis, and public vehicles according to different form characteristics and charging characteristics; The OD start-end matrix is used to simulate the starting and ending points of each EV's trip. The shortest path is planned in combination with the real-time road impedance matrix to obtain a set of travel segments. The initial travel time and return time of each EV, as well as the average travel speed, are simulated using Monte Carlo simulation. Normal distribution is used to simulate the battery capacity of different types of EVs; different types of EVs correspond to different charging demand conditions; EVs are divided into two categories based on whether they accept V2G dispatch: Where: Ω d and Ω nd They represent the set of electric vehicles that can accept V2G dispatch and those that do not accept V2G dispatch respectively; is the EV charging power; when the return time of the i-th vehicle is t i,d Later than departure time t i,o Travel time The sum of the two means that EV can perform V2G power exchange, otherwise it will always be in one-way charging mode.

6. The planning method for an integrated photovoltaic storage and charging station coupled with power and transportation according to claim 5 is characterized in that: In S1, establishing the EV charging load model further includes: obtaining the average EV queue waiting time and average queue length based on the number of vehicles going to the integrated station for charging per hour: Where: The number of EVs that need charging; is the average waiting time in queue for EVs; is the number of charging piles in the integrated station; τ EV is the number of EVs that complete the charging process at each charging pile within a unit time, ρ is the service intensity of the charging pile, and L wait For average captain; Real-time power consumption of electric vehicles considering ambient temperature and speed F Tp The model is: F Tp =K Tp +g Tp ; Where: K Tp At ambient temperature T p The vehicle is moving at a speed v EV The power consumption of EV air conditioner after driving S kilometers; T pmax , T pmin are the upper and lower thresholds for turning on the air conditioner respectively; W L and W R are the cooling and heating power of the air conditioner respectively; γ Tp It is the real-time power consumption per unit mileage at different vehicle speeds.

7. The planning method of the integrated photovoltaic storage and charging station coupled with power and transportation according to claim 1 is characterized in that: Said S1 specifically includes: predicting the number of vehicles required for different OD pairs based on historical data, distributing traffic flow in combination with the actual road network structure, thereby obtaining the number of vehicles and congestion degree of each road section, and thus calculating the road resistance matrix within the time period; Through Monte Carlo simulation of the driving and charging characteristic parameters of each EV, real-time path planning is performed for the EV based on its OD pair demand and the real-time road resistance matrix using the Dijkstra algorithm. During driving, after each road section, it is determined whether the EV has a charging demand. If so, the integrated station installation node is selected according to the real-time road resistance matrix, the path set is updated, and it is determined whether V2G mode power exchange is performed.

8. The planning method for an integrated photovoltaic storage and charging station coupled with electric power and transportation according to claim 1 is characterized in that: The S2 specifically includes: The objective function includes the annualized comprehensive cost function of the integrated station and the distribution network: min C all =C Inv +C om +C FE +Cb uy +C NL +C CL +C BD +C Tra ; Where C Inv is the annualized investment and construction cost, C om The operation and maintenance costs of each equipment, C buy Cost of purchasing electricity from the upper grid, C NL is the system network loss cost, C CL The cost of electric energy loss during EV charging and discharging, C BD is the EV battery degradation cost, C Tra Additional transportation costs incurred by dispatching EV loads; Second-order cone relaxation is used to process the constraints, and the mixed-integer nonlinear programming model is transformed into a mixed-integer second-order cone programming model for solution, thus obtaining the planning result data of the integrated photovoltaic storage and charging station.

9. The planning method for an integrated photovoltaic storage and charging station coupled with power and transportation according to claim 1 is characterized in that: In S2, the constraints include system flow constraints, node voltage constraints, branch current constraints, integrated station equipment output constraints, integrated station equipment installation capacity constraints, electric vehicle participation in V2G constraints, electric vehicle charging and discharging state mutual exclusion constraints, integrated station EV charging and discharging equivalent load constraints, EV owner charging satisfaction constraints, electric vehicle load space scheduling constraints, integrated station charging pile installation quantity constraints, and electric vehicle charge state constraints.

10. The system based on the planning method of the photovoltaic storage and charging integrated station coupled with power and transportation according to any one of claims 1 to 9 is characterized in that: The system includes a framework construction module, a prediction model construction module, a charging load model, a load prediction module, and a planning result solution module; wherein: The framework construction module is used to build a collaborative planning framework for integrated power and transportation coupling stations based on basic data, and to establish a mathematical model of the integrated power and transportation coupling station equipment within the framework; The prediction model building module is used to build a spatiotemporal distribution prediction model of EV load coupled with electricity and transportation; The load forecasting module is used to predict the spatiotemporal distribution of charging load and obtain the spatiotemporal distribution of EV load; The planning result solving module is used to determine the objective function and the day-ahead economic dispatch constraints, and obtain the planning results of the photovoltaic storage and charging integrated station under the power and transportation coupling conditions through solving.

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